微型电脑应用

由于高质量发展的需要，人机交互系统需要完成难度更高、精度更高、更加复杂的任务。在现有的人机交互系统中，手势的分割与识别技术不足是限制人机交互系统向前发展的主要原因。因此，提高手势的分割与识别技术，是实现高效敏捷人机交互系统的关键因素。为实现更高质量的手势动作识别，利用改进后的卷积神经网络对手势动作进行分割与识别。利用数据库中的手势图像对模型进行性能测试。实验结果显示，研究提出的基于卷积神经网络手势分割与识别模型的准确率达到99.07%,具有较高的准确率和效率，可以有效提高手势动作分割与识别的质量，为人机交互系统的改进和应用提供支持。

In view of the need for high-quality development, the human-computer interaction system needs to complete more difficult, accurate and complex tasks. In the existing human-computer interaction system, the lack of gesture segmentation and recognition technology is the main reason that restricts the development of human-computer interaction system. Therefore, the improvement of gesture segmentation and recognition technology is the key factor to achieve an efficient and agile human-computer interaction system. To achieve higher quality gesture recognition, the improved convolutional neural network is used to segment and recognize gesture movements. The performance of the model is tested using gesture images in the database. The experimental results show that the accuracy of the gesture segmentation and recognition model based on convolution neural network is 99.07%, which has high accuracy and efficiency, improves the quality of gesture segmentation and recognition, and provides support for the improvement and application of human-computer interaction system.